Distributed system identification for linear stochastic systems with binary sensors

Abstract

The problem of distributed identification of linear stochastic system with unknown coefficient θ∗ over time-varying networks is considered. For estimating θ∗, each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbors, a stochastic approximation-based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis.